ReviewNature biomedical engineering2023
Tackling prediction uncertainty in machine learning for healthcare.
Review in Nature biomedical engineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
43 citing papers in PubMed.
- The Effectiveness of Machine Learning Algorithms in Predicting Healthcare Service Quality Metrics: A Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
- A Patient Panel Assignment Strategy to Balance Provider Workload in Family Medicine.Healthcare (Basel, Switzerland) · 2026Article
- Implementing trust in non-small cell lung cancer diagnosis with a conformalized uncertainty-aware AI framework.Nature biomedical engineering · 2026Article
- Article
- Foundations of Gerophysics.Aging · 2026Article
- Predicting the course of high-impact chronic pain using machine learning algorithms.The journal of pain · 2026Article
- Multi-scale Multimodal Representation for Enhanced Survival Prediction in Computational Pathology.Interdisciplinary sciences, computational life sciences · 2026Article
- Interpretable Cancer Survival Prediction by Fusing Semantic Labelling of Cell Types and Whole Slide Images.Interdisciplinary sciences, computational life sciences · 2026Article
- Defining operational safety in clinical artificial intelligence systems.NPJ digital medicine · 2026Article
- Pregnancy lab test dynamics resemble rejuvenation of some organs and aging of others.Nature communications · 2026Article
- Article
- Endostrat: a multicenter study of AI-assisted two-level risk stratification for gastric precancerous and early neoplastic lesion screening.Frontiers in medicine · 2026Article
- Do we actually need aging clocks?npj aging · 2025Review
- A multimodal uncertainty-aware AI system optimizes ovarian cancer risk assessment workflow.NPJ digital medicine · 2025Article
- From Lab to Clinic: Artificial Intelligence with Spectroscopic Liquid Biopsies.Diagnostics (Basel, Switzerland) · 2025Review
- Uncertainty-aware ensemble of foundation models differentiates glioblastoma from its mimics.Nature communications · 2025Article
- Mitochondrial methylcytosines as blood-based biomarkers for Alzheimer's disease dementia prognosis.iScience · 2025Article
- Biological Age Estimation From the Age Gap Using Deep Learning Integrating Morbidity and Mortality: Model Development and Validation Study.Journal of medical Internet research · 2025Article
- From large language models to multimodal AI: a scoping review on the potential of generative AI in medicine.Biomedical engineering letters · 2025Review
- Transparent artificial intelligence-enabled interpretable and interactive sleep apnea assessment across flexible monitoring scenarios.Nature communications · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predictive machine-learning systems often do not convey the degree of confidence in the correctness of their outputs. To prevent unsafe prediction failures from machine-learning models, the users of the systems should be aware of the general accuracy of the model and understand the degree of confidence in each individual prediction. In this Perspective, we convey the need of prediction-uncertainty metrics in healthcare applications, with a focus on radiology. We outline the sources of prediction uncertainty, discuss how to implement prediction-uncertainty metrics in applications that require zero tolerance to errors and in applications that are error-tolerant, and provide a concise framework for understanding prediction uncertainty in healthcare contexts. For machine-learning-enabled automation to substantially impact healthcare, machine-learning models with zero tolerance for false-positive or false-negative errors must be developed intentionally.
Indexed as
Identifiers
36581695What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.